As it is mentioned in F1 score Wikipedia that 'F1 score reaches its best value at 1 (perfect precision and recall) and **worst at 0**'.

What is the worst condition that was mentioned?

**Contents**hide

#### Best Answer

Yes. As explained in this answer the F measure can be written as:

$F_{beta}=frac{(beta^2 +1)PR}{R+beta^2P}$

Then if $P$ or $R$ is zero the $F_{beta}$ would be zero, and for the $F_1$ in your case suppose that the true labels for a binary classification problem are 1, 1, 0, 0 but your predictions are 0, 0, 1, 1 both the recall and precision are 0 so the $F_1$ is also 0.

### Similar Posts:

- Solved – Why f beta score define beta like that
- Solved – F-measure for document clustering evaluation – NaN
- Solved – The representation of F1-score on the Precision-Recall Curve
- Solved – The representation of F1-score on the Precision-Recall Curve
- Solved – How to control trade-off between precision and recall